An important component of an automated surveillance environment is person re-identification. The problem is often addressed using data received from vision sensors using appearance-based features, which are heavily reliant on visual cues such as colour, texture, and so on, limiting the reliability of re-identification of an individual. Much research has been performed to solve the problem of re-identification utilising human gait using inertial measurement units (IMU) data, which is thought to be unique and offer a distinct biometric signature that is especially ideal for re-ID in uncontrolled conditions. The locomotive activity of walking was the primary emphasis. The current study utilised not only locomotive activities but also non-locomotive activities of daily living. The data was obtained from the WISDM lab. The data is collected while engaging in six distinct everyday activities. The dataset was originally gathered for the purpose of Human Activity Recognition. Nonetheless, each person is given a unique ID. This information was utilised to re-identify the individual. The dataset consists of data of 36 volunteers. Shanakht-Net, a novel convolutional neural network, is introduced. The F1-score obtained is 93\%. Precision, recall, and accuracy are assessed and reported as well.